Instructions to use llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic")
model = AutoModelForMultimodalLM.from_pretrained("llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic

SGLang

How to use llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic with Docker Model Runner:
```
docker model run hf.co/llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic
```

llmfan46 commited on Jun 11

Commit

ce415cf

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1 Parent(s): e425821

Update README.md

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@@ -24,7 +24,7 @@ base_model:
 ---
-### **90% fewer refusals** (10/100 Uncensored vs 99/100 Original) while preserving model quality (0.0541 KL divergence).
 ## ❤️ Support My Work
 Creating these models takes significant time, work and compute. If you find them useful consider supporting me:
@@ -40,7 +40,7 @@ Your help will motivate me and would go into further improving my workflow and c
 -----
-# This is a decensored version of [google/gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it), made using [Heretic](https://github.com/p-e-w/heretic) v1.2.0 with the [Arbitrary-Rank Ablation (ARA)](https://github.com/p-e-w/heretic/pull/211) method
 ## Abliteration parameters
@@ -59,7 +59,7 @@ Your help will motivate me and would go into further improving my workflow and c
 ## Performance
-| Metric | This model | Original model ([gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it)) |
 | :----- | :--------: | :---------------------------: |
 | **KL divergence** | <span style="color:darkgoldenrod">0.0365</span> | 0 *(by definition)* |
 | **Refusals** | ✅ <span style="color:darkgreen">11/100</span> | ❌ <span style="color:blue">99/100</span> |

 ---
+### **89% fewer refusals** (11/100 Uncensored vs 99/100 Original) while preserving model quality (0.0365 KL divergence).
 ## ❤️ Support My Work
 Creating these models takes significant time, work and compute. If you find them useful consider supporting me:
 -----
+# This is a decensored version of [google/gemma-4-31B-it-qat-q4_0-unquantized](https://huggingface.co/google/gemma-4-31B-it-qat-q4_0-unquantized), made using [Heretic](https://github.com/p-e-w/heretic) v1.2.0 with the [Arbitrary-Rank Ablation (ARA)](https://github.com/p-e-w/heretic/pull/211) method
 ## Abliteration parameters
 ## Performance
+| Metric | This model | Original model ([gemma-4-31B-it-qat-q4_0-unquantized](https://huggingface.co/google/gemma-4-31B-it-qat-q4_0-unquantized)) |
 | :----- | :--------: | :---------------------------: |
 | **KL divergence** | <span style="color:darkgoldenrod">0.0365</span> | 0 *(by definition)* |
 | **Refusals** | ✅ <span style="color:darkgreen">11/100</span> | ❌ <span style="color:blue">99/100</span> |